The goal of this project, sponsored by Dart Neuro-Science, is to generate potential drug targets relating to memory and neural plasticity, focusing specifically on Alzheimer’s disease. We have developed a novel methodology built on existing network analysis algorithms to identify key genes implicated in Alzheimer’s Disease. This methodology can be used to identify potential drug targets for Alzheimer’s, and in the future might also be used towards drug discovery for other diseases.

Helix is a private software platform company for personalized genomic products. The goal of our Clinic project was to integrate sequencing results from Helix and activity tracking via Fitbit, and develop a proof-of-concept to monitor these data in research studies. We built a participant monitoring dashboard and conducted IRB-approved human subject studies to demonstrate the dashboard’s capabilities while examining traits related to fitness and sleep.

Computer Science Clinic

23andMe

23andMe is a bioinformatics company whose mission is to help people access, understand, and benefit from the human genome. 23andMe analyzes DNA to identify specific variations, or alleles, in order to determine ancestry and wellness traits. 23andMe makes much of this information available to third party developers through a public API. The goal of this project is to create an application which will help encourage other developers to use the public API. Therefore, the application should serve as an example of how to correctly access the public API and securely handle a user’s genetic information. Additionally, we aim to engage users while their data is being analyzed.

3D asset management can pose a significant problem for Augmented and Virtual Reality (AR/VR) applications. The Harvey Mudd Clinic Team is working with Accenture Labs to make AR/VR content management easier by providing a straightforward workflow allowing users to alter their content from within existing tools. The team’s Unity plugin allows inexperienced users to understand the tradeoffs between the visual quality of their assets and app performance, and then manage that tradeoff by customizing their asset’s polygon counts.

Amazon Music

Our project focuses on creating an enhanced infrastructure to enable Amazon’s intelligent personal assistant, Alexa, to respond to a broader and deeper range of queries about music. To this end, we developed an ontology (graphical knowledge structure) for recorded music and prototyped a database-backed, text-based web interface based on the ontology, which demonstrates responses to sample queries.

Amazon Prime Now wants to automate the process of identifying products whose images do not match their associated titles on their website. Our team has designed and implemented a solution using various machine learning techniques to automatically identify these problematic listings.

Barefoot Networks

Barefoot Networks builds high-speed programmable networking devices. Our team is building a configurable network traffic tester that can handle up to 6.4Tbps of traffic–enough for every student at the Claremont Colleges to simultaneously stream 200 high-definition movies. The tester will send varying traffic patterns to the device under test, which will immediately forward the packets back to the tester. The tester will analyze packet delay, sequencing, and loss, and will gather and report relevant statistics.

Consensus Corporation makes point-of-activation software for the sale of mobile phones. This software includes a risk prediction engine, which predicts whether or not a phone plan will be deactivated and the sale deemed fraudulent. The Consensus Clinic Team’s project is twofold: (1) improve this risk prediction engine by incorporating new features and analyzing different machine learning models and (2) detect unintended bias in the risk prediction engine.

Intuitive Surgical

Intuitive Surgical specializes in minimally invasive robot-assisted surgery and they developed the da Vinci surgical system. The objective of our project is to analyze the motion of surgical instruments controlled by surgeons and video recording of surgeries by developing computational models that automatically recognize suturing activities during robot-assisted surgery. Our approach comprises machine learning and neural network architectures. These computational models could then be used to generate advanced analytics such as surgeon performance reports.

Supercomputers provide the computing power for complex physics simulations, but these simulations require frequent manual adjustments to pre-vent run-time failures. Machine learning is a potential solution for automating this process. The LLNL clinic team is developing a machine learning model appropriate for supercomputers that can learn from the output of physics simulations as they run in real time.

The objective of this project is to explore the possibility of using augmented reality (AR) while driving a car. This entails comparing various AR headsets such as the Microsoft Hololens and the Meta 2, and building a functioning prototype that addresses a moving car use-case. The headset comparison involves testing devices in a stationary and moving vehicle, and understanding their respective development experiences. Our prototype application attempts to help drivers park more safely and precisely, and serves as a starting point for the Mercedes-Benz team for an extendable AR headset prototyping framework.

NASA AMES Research Center

Our project aims to provide NASA with new scheduling protocols for multi-agent systems (e.g., a robot team). In such systems, scheduling disturbances (due to environmental factors or equipment malfunction) may necessitate rescheduling. However, communicating new schedules to all agents can be resource intensive. Reducing rescheduling can improve the performance of multi-agent systems when communication resources are limited. Our team has designed three algorithms to minimize rescheduling, trading communication for schedule quality. Further, we developed infrastructure for testing such algorithms.

Have you ever wondered how your website is performing? Wouldn’t it be convenient to know if your website is going to experience problems before they happen? The New Relic Clinic Team is exploring new ways to visualize website analytics data, communicate application health, and predict spikes in errors. Using animation, machine learning, and deep learning, the New Relic Clinic Team is developing innovative and unique solutions for New Relic’s customers.

Proofpoint, Inc.

Proofpoint is a leader at identifying online threats. This project seeks to more accurately classify phishing webpages based on their look, i.e., their visual rendering within a browser. The team designed, piloted, and tested software tools to support this investigation, culminating in a machine-learning pipeline that estimates the probability that a page is phishing based on its screen capture.

Our project aims to expand the capabilities of Rapid7’s security assessment platform by integrating the company’s newly-developed container assessment service into popular continuous integration tools, namely Jenkins, Bamboo, and Teamcity. Anyone developing a container with these tools can add our plugin to the build pipeline to check for vulnerabilities during each build. The plugin generates a detailed assessment report, and the user can configure rules to pass or fail the build depending on various criteria present in the assessment results.

Steelcase is looking to ensure color consistency before large purchase orders for their wood veneer furniture. In order to reduce waste due to veneer color issues, our clinic team is developing a portable veneer classification device that can verify veneer colors objectively in uncertain lighting conditions. The prototype is a mobile lightbox with an intuitive graphical user interface and a computer vision classification system.

In this project, the goals were to aggregate DNS-level data, apply machine learning approaches to identify command and control (C&C) botnets through automated analysis of live traffic patterns, and construct a website for dynamic visualization of threats. Visualizations help the user pinpoint where botnet attacks are coming from, identify geographic hotspots for botnet activity, and find out who is at risk for infection.

ATI Metals manufactures high performance metal products for the nuclear energy, chemical processing, and aerospace industries. ATI’s customers require parts fabricated to precise dimensions. The ATI clinic team has integrated computer vision techniques and laser rangefinders into an automated measurement system that generates a 3D model of metal components post-process on the hot rolling mill. This system will streamline ATI’s production by detecting out-of-spec parts earlier in the process, and assist process engineers in improving product consistency.

The 2017-18 BD Biosciences Clinic Team improved a quality control (QC) system for an acoustic microfluidic chip. This chip separates white blood cells, which are of primary interest for the detection of autoimmune disorders, from the rest of blood. The QC system acquires images of flow through the chip and reports the accuracy of blood separation through image processing techniques. The team improved mechanical design and image processing components of this system to reduce manufacturing cost and processing time.

Ceremod, Inc. is a startup founded at Oregon Health & Science University to foster innovation in medical technology. The Ceremod, Inc. clinic team has modeled and designed a flexible, biocompatible subsystem to cool a small target region in the human body. The modeling results are being used in feasibility and design studies of the experimental and production devices.

The 2017-18 COH-Raman team aimed to reduce the collection time for Raman spectra of breast tissue to 1-3 seconds. This spectral database was analyzed using machine learning algorithms to display the probability of breast cancer. The spectral analysis is driven by understanding the bio-chemical markers that are evident in each unique Raman spectrum. The team also investigated Surface Enhanced Raman Spectroscopy (SERS) to enhance the Raman spectra. Additionally, the team worked on combining Raman diagnostics with laser ablation treatment.

The City of Hope Wireless Clinic team is designing low-cost and wireless laparoscopic cameras and smoke evacuators. In laparoscopic surgery, smoke is a byproduct that must be evacuated because it impedes visibility and is potentially hazardous, ultimately prolonging surgery. In addition to designing the systems to be wireless, the team is incorporating recirculation of insufflated air into the smoke evacuation system and first-person view (FPV) VR technology into the camera system. With their designs, the team aims to improve visualization of the abdominal cavity during laparoscopic surgery and increase access to laparoscopic surgical tools.

The Doosan Bobcat clinic team will develop a proof of concept for autonomously driving a compact track loader to connect to a bucket attachment. Assumed starting conditions include flat, level ground and close alignment of the attachment and loader. Sensors, such as LiDAR sensors, will be used to determine the position of the vehicle with respect to the bucket. This position will be fed into the control system, which will send desired commands to the tracks on the loader.

Eaton Corp manufactures high voltage circuit breakers and related electrical safety devices. Ground and Test Devices (GTD) are used to ground the line or load side of the power bus bars for maintenance activities. The Eaton Clinic Team has been enlisted to improve design flaws in a GTD that was pulled from the market when updated standards from the Institute of Electrical and Electronics Engineers (IEEE) were released.

FarmX, a San Francisco startup, aims to save up to 2% of California’s water by providing precision irrigation management. The HMC FarmX clinic team improved two sensor systems: a weather station, which measures the farm’s local water cycle, and a dendrometer, which monitors tree health. These sensor systems will help FarmX provide better irrigation recommendations and alert farmers when their crops are in danger.

Georg Fischer Signet is an engineering firm that produces flow and analytical technology. The 2017-2018 Georg Fischer Signet Clinic Team aims to design, build, and test a corrosion-proof method of internally processing and communicating pH and temperature measurements to a remote data collection network. To avoid corrosion, the project implements wireless power through inductive coupling and wireless communication through Bluetooth Low Energy. A LoRa network architecture is used to receive data from multiple units.

The GKN Aerospace Clinic Team is designing, implementing, and testing a new software system to locate and quantify optical distortions in cockpit windows, fighter jet canopies, and other aircraft transparencies. The software uses modern computer vision and data analysis tools to improve upon the industry standard for optical distortion mapping. GKN Aerospace will use our solution to prevent expensive damage on aircraft canopies and achieve higher product quality.

The goal of this project is to explore the predictive diagnostic value of the complex spectra generated by metabolites adsorbed onto the HP Surface Emission Raman Spectroscopy substrates from cancerous and healthy cervical tissue in lab. The project has two primary components. The laboratory component entails cell culturing and preparation of the samples for data collection. Then when the data is collected, machine learning algorithms will be used to distinguish healthy from cancerous cell by the SERS spectra.

HP, Inc.

Study of Carbon Black Dispersion in Polyurethane: Impact on Mechanical and Electrical Properties

The HP CB Clinic Team studied polyurethane (PU)-carbon black (CB) composites as new elastomer materials for Binary Ink Developers (BIDs) on HP Indigo printing presses. The investigation focused on how CB dispersion and loading affect the mechanical and electrical properties of the composites to meet HP’s product functionality and reliability requirements. Techniques such as tensile/tear, voltage-current sweeping, and SEM imaging analysis were used for material characterization.

As part of the nuclear terrorism prevention effort at Lawrence Livermore National Laboratory (LLNL), the LLNL Clinic Team is challenged with testing and improving the performance of a “smart” network of pocket-size radiation sensors. This will be done by 1) using non-threat radiation to monitor the calibration of the sensors, 2) configuring sensors to work together to improve detection sensitivity and confidence, and 3) integrating location information of known radiation sources to reduce false alarm rates. LLNL is operated under Contract DE-AC52-07NA27344.

Meggitt Control Systems is an aerospace company that produces a variety of products for extreme environments. The Meggitt clinic team was tasked with researching, testing and characterizing low-cost, environmentally friendly surface treatments to reduce wear on a butterfly bleed-air valve. These valves are used in aircraft engines and must withstand high temperatures while experiencing as little wear as possible over 50,000 cycles.

The team designed and built an iodine feed system which stores solid iodine for a minimum of five years, and sublimates solid iodine to deliver gaseous iodine at a rate of at least 3 mg/s within 10 minutes in a zero gravity setting. The team validated this proof of concept design through rigorous thermal and structural modeling, in addition to testing the proof of concept for corrosion, leak resistance, flow control, thermal management, and propellant management.

Strain wave gear reduction technology has dominated the aerospace rotary transmission industry for half a century due to its simplicity, efficiency, and accuracy. Moog, Inc. has sponsored a Harvey Mudd Clinic team to develop an alternative for use in the sponsor’s satellite actuators. The team’s solution relies on hypocycloidal geometry to achieve a high reduction ratio, zero backlash, and superior performance.

This project focuses on the P6 gallon bottle blow-molding machine at Niagara’s Corporate plant and finding physical limitations to speed increase. The team is characterizing the dynamics of the machine and finding resonant frequencies at which the machine vibrates due to its operation. By finding what is most likely to break first due to vibration, and then strengthening it, the team can safely increase the speed of operation.

The Systron Donner Inertial (SDI) clinic team has been tasked with designing, writing, and judging the performance of an improved gyroscope bias compensation algorithm. The goal of the project is to reduce rate error by a factor of 10. This goal will be reached with the assistance of statistical analysis and an embedded neural network which will allow the team to explore new causes in gyroscope bias.

Techmation

Project Beluga: Perception System and State Estimation in GPS-Denied Environment

The Techmation Clinic team selected and implemented an acoustic beacon and hydrophones to augment an IMU in an Autonomous Underwater Vehicle (AUV). The team then used these sensors for state estimation of the AUV in a GPS-denied environment. A major part of this process was also the development of an Extended Kalman Filter, a state estimation algorithm for sensor integration, and a localization algorithm for the acoustic system.

The Toyota clinic project aims to investigate alternative methods of storing and utilizing energy recovered in the regenerative braking process of Toyota’s hydrogen fuel cell drayage truck. This involves researching and ideating various energy storage solutions and quantifying the amount of energy that can be recovered along several drayage routes from the Port of Los Angeles to Long Beach.

The WET Clinic team built a fleet of small aquatic robots to perform light shows. The choreography of these shows uses a computer vision system to precisely localize the pods and a multilink Bluetooth low energy system to communicate with the pods. The fleet of pods uses multi-robot trajectory planning algorithms to move in a choreographed sequence.

Engineering/Mathematics Clinic

Niagara Bottling

This project has aimed to uncover inefficiencies at Niagara Bottling by taking a more holistic approach to the problem of optimizing transportation processes across Niagara’s facilities. The Harvey Mudd team has investigated potential cost-reduction measures through the application of operations research and statistical methods. The end goal of this project has been to optimize total supply chain costs across facilities as well as provide invaluable business insights to Niagara.

Claremont Locally Grown Power is a program of CHERP Inc., (Community Home Energy Retrofit Project), a California-based 501(c)(3) non-profit social enterprise, in an exclusive licensing agreement to deploy idealPV’s patented solar technology. The clinic team is assisting CLGP in their goal of outfitting lower-to-middle income households with cheaper, safer, and more efficient solar panels by providing third-party verification and testing of the underlying idealPV technology through lab research, physics-based mathematical modeling, and prototype comparison field studies.

Sandia National Laboratories

Measuring the Permittivity of Ferroelectric Nanoparticles in an Epoxy Composite

Barium titanate (BTO) is a ferroelectric material commonly used in capacitors because of its high bulk dielectric constant, which may be even higher in nanoparticle form. We are determining the dielectric constant of BTO nanoparticles as a function of particle size by measuring composites of BTO nanoparticles in epoxy. We’re using ball-milling alongside surfactants to reduce nanoparticle ag-glomeration, examining particle geometry using scanning electron microscopy, and using finite element analysis to extract the dielectric constant of the nanoparticles.

Kibera is a large informal settlement in Nairobi, Kenya where annual flash flooding causes significant health, economic, and structural problems in the community. Current drainage infrastructure consists of informal channels that are not designed to handle the volume of water that falls during large rain events. The objective of this project is to work with the community to develop practical design improvements for the existing drainage channels that can be implemented in Kibera, to improve quality of life for residents.

Mathematics Clinic

EDR

Our task is to automatically detect and read the handwritten addresses from EDR’s collection of 1.2 million Sanborn maps. Sanborn maps are detailed city maps produced regularly between 1880 and 2006. We use various image processing techniques to first find the street segments, and then detect the handwritten street names and house numbers. We then run these images through our OCR model, which reliably parses connected characters that are rotated or skewed.

Intel Sports

Intel’s TrueView technology can reconstruct three-dimensional video at any location in a stadium using multiple video recordings. However, this system does not extend to audio. This project involves developing an approach to reconstruct game audio at any location based on microphone recordings, to combine with the existing volumetric video system. After exploring existing algorithms for sound source separation and localization, we are developing a prototype system called auVVio that extends these techniques to interactively reconstruct game sounds in 3D.

Microsoft Bing

Bing is the second most popular search engine in the United States. Bing’s Livesite Engineering team collects system performance metrics to detect incidents (outages, etc.) that negatively impact the end user experience. These incidents result in a loss of ads revenue and users. Identifying root cause of such incidents requires sifting through a lot of metrics and narrowing down the root cause to one or more key metrics. Our clinic team is working to streamline and automate this process by leveraging statistical models and methods.